Abstract
The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OPF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure. The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OPF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Breiman, L.: Bagging predictors. Machine Learning Journal 2(24), 123–140 (1996)
Breiman, L.: Pasting small votes for classification in large databases and on-line. Machine Learning 36, 85–103 (1999)
Breve, F.A., Ponti Jr., M.P., Mascarenhas, N.D.A.: Multilayer perceptron classifier combination for identification of materials on noisy soil science multispectral images. In: XX Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2007), pp. 239–244. IEEE, Belo Horizonte (2007)
Brown, G., Kuncheva, L.I.: “Good” and “Bad” Diversity in Majority Vote Ensembles. In: El Gayar, N., Kittler, J., Roli, F. (eds.) MCS 2010. LNCS, vol. 5997, pp. 124–133. Springer, Heidelberg (2010)
Chawla, N.V., Hall, L.O., Bowyer, K.W., Moore Jr., T.E.: Distributed pasting of small votes. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS, vol. 2364, pp. 52–62. Springer, Heidelberg (2002)
Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
Freund, T.: Boosting: a weak learning algorithm by majority. Information and Computation 121(2), 256–285 (1995)
Ho, T.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
Kuncheva, L., Whitaker, C., Shipp, C.A., Duin, R.: Limits on the majority vote accuracy in classifier fusion. Pattern Analysis and Applications 6, 22–31 (2003)
Lee, W.J., Duin, R.: A labelled graph based multiple classifier system. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds.) MCS 2009. LNCS, vol. 5519, pp. 201–210. Springer, Heidelberg (2009)
Li, J., Wang, J.Z.: Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans Pattern Analysis and Machine Intelligence 25(9), 1075–1088 (2003)
Papa, J.P., Falcão, A.X., Suzuki, C.T.N.: LibOPF: a library for optimum-path forest (OPF) classifiers. (2009), http://www.ic.unicamp.br/~afalcao/libopf/
Papa, J.P., Falcão, A.X., Suzuki, C.T.N.: Supervised pattern classification based on optimum-path forest. Int. J. Imaging Systems and Technology 19(2), 120–131 (2009)
Schenker, A., Bunke, H., Last, M., Kandel, A.: Building graph-based classifier ensembles by random node selection. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 214–222. Springer, Heidelberg (2004)
Skurichina, M., Duin, R.P.W.: Bagging, boosting and the random subspace method for linear classifiers. Pattern Analysis and Applications 5, 121–135 (2002)
Woods, K., Kegelmeyer Jr., W., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Trans Pattern Analysis and Machine Intelligence 19(4), 405–410 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ponti, M.P., Papa, J.P. (2011). Improving Accuracy and Speed of Optimum-Path Forest Classifier Using Combination of Disjoint Training Subsets. In: Sansone, C., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2011. Lecture Notes in Computer Science, vol 6713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21557-5_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-21557-5_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21556-8
Online ISBN: 978-3-642-21557-5
eBook Packages: Computer ScienceComputer Science (R0)